1. Technical Field
The present invention is directed to the field of data mining. More specifically, the present invention is directed to a multi-dimension analysis computer system for understanding outcomes of business activities.
2. Description of Related Art
Enterprises generate large volumes of transactional data that are generally stored in a data warehouse or an On-Line Analytical Processing (OPAL) system. This transactional data contains information on the outcomes of enterprise operations. For example, a for-profit business will have records of which customers bought what products. Similarly, a government agency may have records on which people requested what services. Likewise, a non-profit organization may have records of which donors gave money to what projects.
The enterprise data warehouse also contains the characteristics of the business that are related to the transactions. For example, a for-profit business may track the sales by region, and may have additional information about each sales region, such as the number of sales people and the number of years of experience for each sales person. Similarly, a government agency may track the methods of requesting services and the methods of responding to those requests. Similarly, the non-profit organization may track the activities of each of its projects by region and the amount of publicity for each project.
Additionally, the enterprise's data warehouse may have data on the characteristics of the customers, people, and donors, (that is, the transactors) who made the transactions. One characteristic that is often known is the location of the transactor. Other characteristics depend on the enterprise. For example, a business-to-business for-profit company may know what industry its customer is in. Similarly, a government agency may know the person's social security number. And a non-profit organization may know whether the donor is a volunteer at the organization.
Thus, an enterprise may have three or more types of data: (a) data on outcomes, (b) data on business characteristics and (c) data on transactor characteristics. This data can be merged and analyzed to provide information that the enterprise can use to respond to the transactors or potential transactors in order to increase the probability of a good outcome.
One way to analyze the transactional data is to understand what market segments are represented in the data. This understanding may lead to many different operating decisions for the enterprise. For example: (1) targeted advertising campaigns for each different market segment or group of related market segments may be prepared, (2) targeted products, services, and programs can be prepared for different market segments, and (3) employees can be assigned to train in, answer questions from, and consult with the different market segments. One would appreciate that there is an unlimited number of other business decisions that might be made through the understanding of these market segments. Generally speaking, by understanding the market, the enterprise can better understand the interests of the transactors and better prepare to improve the outcomes of the transactions.
In the past, given smaller amounts of data, enterprises could develop an intuition about the market segments by reviewing each transactor's account or by scanning the data manually. Business analysts used statistical software tools to separate the transactions into groups or clusters, whose data could be reviewed to form an intuition about the market segments. Now, with large volumes of data, particularly from transactions on the Internet, knowing the transactors personally and scanning “by eye” are impractical. Similarly, the number of statistical runs that would be required becomes impractical. And thinking in terms of the previous approaches' way of categorizing the market seems less and less useful.
Thus, previous approaches do not include an effective automated system to define market segments in a way that is most meaningful for understanding the outcomes of business activities given the large volume of data collected and maintained by an enterprise. Stated another way, previous approaches do not have an effective automated system that defines a relatively small (manageable) number of market segments.